* DeepFM: a factorization-machine based neural network for CTR prediction on Criteo dataset.
* DeepLabV3: significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2007 semantic image segmentation benchmark.
* Faster-RCNN: towards real-time object detection with region proposal networks on COCO 2017 dataset.
* GoogLeNet: a deep convolutional neural network architecture codenamed Inception V1 for classification and detection on CIFAR-10 dataset.
* Wide&Deep: jointly trained wide linear models and deep neural networks for recommender systems on Criteo dataset.
* Frontend and User Interface
* Complete numpy advanced indexing method. Supports value and assignment through tensor index.
* Some optimizers support separating parameter groups. Different parameter groups can set different `learning_rate` and `weight_decay`.
* Support setting submodule's logging level independently, e.g. you can set logging level of module `A` to warning and set logging level of module `B` to info.
* Support weights to be compiled according to shape to solve the problem of large memory overhead.
* Add some operators implement and grammar support in pynative mode. To be consistent with graph mode.
* User interfaces change log
* Learning rate and weight decay making group params([!637](https://gitee.com/mindspore/mindspore/pulls/637))
* Support weights to be compiled according to shape([!1015](https://gitee.com/mindspore/mindspore/pulls/1015))
* delete some context param([!1100](https://gitee.com/mindspore/mindspore/pulls/1100))
* Fix dropout,topK and addn errors in PyNative mode ([!1285](https://gitee.com/mindspore/mindspore/pulls/1285), [!1138](https://gitee.com/mindspore/mindspore/pulls/1138), [!1033](https://gitee.com/mindspore/mindspore/pulls/1033)).
* Fix memory leaks after execution in PyNatvie mode ([!1201](https://gitee.com/mindspore/mindspore/pulls/1201)).
* Fix HCCL failure in some special scenes ([!1204](https://gitee.com/mindspore/dashboard/projects/mindspore/mindspore/pulls/1204), [!1252](https://gitee.com/mindspore/dashboard/projects/mindspore/mindspore/pulls/1252)).
* Fix Topk operator selection strategy bug between aicore and aicpu([!1367](https://gitee.com/mindspore/dashboard/projects/mindspore/mindspore/pulls/1367)).
* Fix input memory size of 'assign' op unequal in control sink mode when assigning a data from one child graph to another child graph([!802](https://gitee.com/mindspore/dashboard/projects/mindspore/mindspore/pulls/802)).
* Fix allreduce ir inconsistency([!989](https://gitee.com/mindspore/dashboard/projects/mindspore/mindspore/pulls/989)).
* GPU platform
* Fix summary for gradient collection ([!1364](https://gitee.com/mindspore/mindspore/pulls/1364))
* Fix the slice operator ([!1489](https://gitee.com/mindspore/mindspore/pulls/1489))
* Data processing
* Fix memory problems of GeneratorDataset of sub-process ([!907](https://gitee.com/mindspore/mindspore/pulls/907))
* Fix getting data timeout when training the cifar10 dataset under the lenet([!1391](https://gitee.com/mindspore/mindspore/pulls/1391))
RUN pip install--no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.3.0-alpha/MindSpore/cpu/x86_ubuntu/mindspore-0.3.0-cp37-cp37m-linux_x86_64.whl
RUN pip install--no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.3.0-alpha/MindSpore/cpu/ubuntu_x86/mindspore-0.3.0-cp37-cp37m-linux_x86_64.whl
RUN pip install--no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.3.0-alpha/MindSpore/gpu/cuda-10.1/mindspore_gpu-0.3.0-cp37-cp37m-linux_x86_64.whl
RUN pip install--no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.3.0-alpha/MindSpore/gpu/ubuntu_x86/cuda-10.1/mindspore_gpu-0.3.0-cp37-cp37m-linux_x86_64.whl